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Separating Moral Hazard From Adverse Selection And Learning In Automobile Insurance: Longitudinal Evidence From France

Author

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  • Georges Dionne
  • Pierre-Carl Michaud
  • Maki Dahchour

Abstract

The identification of information problems in different markets is a challenging issue in the economic literature. In this paper, we study the identification of moral hazard from adverse selection and learning within the context of a multi-period dynamic model. We extend the model of Abbring et al. (2003) to include learning and insurance coverage choice over time. We derive testable empirical implications for panel data. We then perform tests using longitudinal data from France during the period 1995-1997. We find evidence of moral hazard among a sub-group of policyholders with less driving experience (less than 15 years). Policyholders with less than 5 years of experience have a combination of learning and moral hazard, whereas no residual information problem is found for policyholders with more than 15 years of experience.
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Suggested Citation

  • Georges Dionne & Pierre-Carl Michaud & Maki Dahchour, 2013. "Separating Moral Hazard From Adverse Selection And Learning In Automobile Insurance: Longitudinal Evidence From France," Journal of the European Economic Association, European Economic Association, vol. 11(4), pages 897-917, August.
  • Handle: RePEc:bla:jeurec:v:11:y:2013:i:4:p:897-917
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    File URL: http://hdl.handle.net/10.1111/jeea.2013.11.issue-4
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    Cited by:

    1. Loukas Balafoutas & Rudolf Kerschbamer & Matthias Sutter, 2017. "Second‐Degree Moral Hazard In A Real‐World Credence Goods Market," Economic Journal, Royal Economic Society, vol. 127(599), pages 1-18, February.
    2. Georges Dionne, 2012. "The Empirical Measure of Information Problems with Emphasis on Insurance Fraud and Dynamic Data," Cahiers de recherche 1233, CIRPEE.
    3. repec:ipf:psejou:v:42:y:2018:i:42:p:45-65 is not listed on IDEAS
    4. Shi, Peng & Valdez, Emiliano A., 2011. "A copula approach to test asymmetric information with applications to predictive modeling," Insurance: Mathematics and Economics, Elsevier, vol. 49(2), pages 226-239, September.
    5. Biener, Christian & Eling, Martin & Landmann, Andreas & Pradhan, Shailee, 2018. "Can group incentives alleviate moral hazard? The role of pro-social preferences," European Economic Review, Elsevier, vol. 101(C), pages 230-249.
    6. Peng Shi & Wei Zhang, 2016. "A Test of Asymmetric Learning in Competitive Insurance With Partial Information Sharing," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 83(3), pages 557-578, September.
    7. Georges Dionne & Nathalie Fombaron & Neil Doherty, 2012. "Adverse Selection in Insurance Contracting," Cahiers de recherche 1231, CIRPEE.
    8. Georges Dionne & Kili Wang, 2013. "Does insurance fraud in automobile theft insurance fluctuate with the business cycle?," Journal of Risk and Uncertainty, Springer, vol. 47(1), pages 67-92, August.
    9. Ciprian Matis & Eugenia Matis, 2013. "Asymmetric Information In Insurance Field: Some General Considerations," Annales Universitatis Apulensis Series Oeconomica, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, vol. 1(15), pages 1-17.
    10. Vukina, Tomislav & Nestić, Danijel, 2015. "Do people drive safer when accidents are more expensive: Testing for moral hazard in experience rating schemes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 71(C), pages 46-58.
    11. Dionne, Georges & Michaud, Pierre-Carl & Pinquet, Jean, 2013. "A review of recent theoretical and empirical analyses of asymmetric information in road safety and automobile insurance," Research in Transportation Economics, Elsevier, vol. 43(1), pages 85-97.
    12. Bernard Salanié, 2017. "Equilibrium in Insurance Markets: An Empiricist’s View," The Geneva Papers on Risk and Insurance Theory, Springer;International Association for the Study of Insurance Economics (The Geneva Association), vol. 42(1), pages 1-14, March.
    13. Magali Chaudey, 2017. "Why test the theory of incentives in a dynamic framework?," Working Papers 1733, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    14. Dionne, Georges & Liu, Ying, 2017. "Effects of Insurance Incentives on Road Safety: Evidence from a Natural Experiment in China," Working Papers 17-1, HEC Montreal, Canada Research Chair in Risk Management.
    15. repec:bla:jrinsu:v:84:y:2017:i:4:p:1103-1126 is not listed on IDEAS
    16. repec:pal:gpprii:v:43:y:2018:i:1:d:10.1057_s41288-017-0055-2 is not listed on IDEAS
    17. repec:bla:jrinsu:v:84:y:2017:i:4:p:1269-1293 is not listed on IDEAS

    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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